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8th International Symposium on Intelligent Informatics, ISI 2023

作者： Shenbaga Moorthy, Rajalakshmi Arikumar, K.S. Prathiba, Sahaya Beni Pabitha, P. Sri Ramachandra Faculty of Engineering and Technology Sri Ramachandra Institute of Higher Education and Research Chennai600 116 India Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chennai603 203 India Centre for Cyber Physical Systems School of Computer Science and Engineering Vellore Institute of Technology Chennai600127 India Department of Computer Technology Anna University Madras Institute of Technology Campus Chennai600044 India

ISBN: (纸本)9789819721467

Accurate solutions are needed for a variety of computer vision applications, including medical imaging, object detection, and recognition. Such complicated challenges are beyond the capabilities of artificial intelligence and machine learning, which mainly rely on data and algorithms to learn. The path to Deep Learning (DL) is thus paved. Even though deep learning is incredibly effective at solving complex problems, it is invariant, meaning that it ignores the spatial relationship between the features and instead searches for features only. This can cause the model’s performance to suffer. Additionally, because the model is invariant, gathering a large volume of training data is a difficult undertaking. To address this, the Capsule Network (CapsNet), whose performance outperforms Deep Learning techniques, was introduced. Despite its success, applications of CapsNet and working remain a mystery. Thus, the performance of CapsNet across many applications and obstacles is examined in this study together with the predecessors of CapsNet. Additionally, this paper explores the use of Capsule Networks with an attention mechanism (CapsNet-ATM) for predicting potato leaf diseases. The performance of CapsNet-ATM in this crucial agricultural application is thoroughly assessed and contrasted with the well-known models VGG-16 and VGG-19, providing insight into the system’s efficacy and promise as a reliable tool for the detection and prediction of potato leaf disease. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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REINFORCE Adversarial Attacks on Large Language Models: An Adaptive, Distributional, and Semantic Objective

arXiv

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arXiv 2025年

作者： Geisler, Simon Wollschläger, Tom Abdalla, M.H.I. Cohen-Addad, Vincent Gasteiger, Johannes Günnemann, Stephan Department of Computer Science Munich Data Science Institute Technical University of Munich Germany Google Research United States

To circumvent the alignment of large language models (LLMs), current optimization-based adversarial attacks usually craft adversarial prompts by maximizing the likelihood of a so-called affirmative response. An affirmative response is a manually designed start of a harmful answer to an inappropriate request. While it is often easy to craft prompts that yield a substantial likelihood for the affirmative response, the attacked model frequently does not complete the response in a harmful manner. Moreover, the affirmative objective is usually not adapted to model-specific preferences and essentially ignores the fact that LLMs output a distribution over responses. If low attack success under such an objective is taken as a measure of robustness, the true robustness might be grossly overestimated. To alleviate these flaws, we propose an adaptive and semantic optimization problem over the population of responses. We derive a generally applicable objective via the REINFORCE policy-gradient formalism and demonstrate its efficacy with the state-of-the-art jailbreak algorithms Greedy Coordinate Gradient (GCG) and Projected Gradient Descent (PGD). For example, our objective doubles the attack success rate (ASR) on Llama3 and increases the ASR from 2% to 50% with circuit breaker defense.1 © 2025, CC BY.

关键词： Adversarial machine learning

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How Contaminated Is Your Benchmark? Quantifying dataset Leakage in Large Language Models with Kernel Divergence

arXiv

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arXiv 2025年

作者： Choi, Hyeong Kyu Khanov, Maxim Wei, Hongxin Li, Yixuan Department of Computer Sciences University of Wisconsin–Madison United States Department of Statistics and Data Science Southern University of Science and Technology China

dataset contamination, where evaluation datasets overlap with pre-training corpora, inflates performance metrics and undermines the reliability of model evaluations. Quantifying dataset contamination thus becomes essential to ensure that performance evaluations genuinely reflect a model’s ability to generalize to unseen data, rather than relying on memorized examples. To address this problem, we propose Kernel Divergence Score (KDS), a novel method that quantifies dataset contamination by computing the divergence between the kernel similarity matrix of sample embeddings, before and after fine-tuning on the benchmark dataset. Leveraging the insight that fine-tuning affects unseen samples more significantly than seen ones, KDS provides a reliable measure of contamination. Through extensive experiments on controlled contamination scenarios, KDS demonstrates a near-perfect correlation with contamination levels and outperforms existing baselines. Additionally, we perform comprehensive ablation studies to analyze the impact of key design choices, providing deeper insights into the components and effectiveness of KDS. These ablations highlight the importance of leveraging fine-grained kernel-based information and confirm the reliability of the proposed framework across diverse datasets and settings. © 2025, CC BY.

关键词： Information leakage

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A secure quantum homomorphic encryption ciphertext retrieval scheme

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Soft Computing 2025年第3期29卷 1497-1509页

作者： Cheng, Zhen-Wen Chen, Xiu-Bo Xu, Gang Chang, Yan Miao, Li-Hua Yang, Yi-Xian Wang, Ya-Lan School of Data and Computer Science Shandong Women’s University Jinan250300 China Information Security Center State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China School of Information Science and Technology North China University of Technology Beijing100144 China School of Cybersecurity Chengdu University of Information Technology Chengdu610225 China Huawei Technologies Co. Ltd Shenzhen518129 China Department of Computer Science Faculty of Engineering and Physical Sciences University of Surrey Surrey GuildfordGU2 7XH United Kingdom

In a recent paper (Gong et al. Quantum Inf Process 19:3, 2020), a novel ciphertext retrieval scheme based on the Grover algorithm and quantum homomorphic encryption was presented. In this scheme, when the server performs the operation of marking the solution on the user’s encrypted state in the Grover iteration, it needs to remove many gate-errors generated in the homomorphic evaluation of the T gate. And the server could judge this specific solution from the quantum circuit of marking the solution. It makes this scheme unable to achieve the low-cost and secure ciphertext retrieval. Therefore, we improve the Gong et al.’s scheme and propose a secure quantum homomorphic encryption ciphertext retrieval scheme. In our scheme, the trusted third party is introduced to cooperate with the server to execute the Grover algorithm. In each Grover iteration, the trusted third party can quickly mark the solution on the plaintext state, encrypt the marked state, and transmit it to the server. Then the server performs the remaining operations of this Grover iteration on the encrypted state. The trusted third party finally decrypts the iterated state. This cooperative approach ensures that the number of auxiliary qubits required and extra quantum gates executed in our scheme are lower than the Gong et al.’s scheme. By analyzing the security of our scheme, we confirm that the server and the trusted third party will not be informed of this solution. Thus, our scheme realizes the secure ciphertext retrieval with low computational overhead. We utilize IBM’s Qiskit framework to simulate our scheme, and the experimental result shows that our scheme is correct. It is worth noting that the low-cost and secure ciphertext retrieval will play a crucial role in modern information security and privacy protection. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Ciphertext

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Incentivizing Crowdsensing for DT-Enabled Metaverse 20th

Incentivizing Crowdsensing for DT-Enabled Metaverse

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20th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2024

作者： Ye, Dongdong Huang, Xumin Wu, Yuan Kang, Jiawen Zhong, Weifeng Niyato, Dusit School of Automation Guangdong University of Technology Guangzhou China State Key Laboratory of Internet of Things for Smart City University of Macau Taipa China Department of Computer and Information Science University of Macau Taipa China Collge of Computing and Data Science Nanyang Technological University Singapore Singapore

ISBN: (纸本)9789819628292

Digital Twin (DT) has been proposed as a key enabling technology to produce a digital clone of the physical world and facilitate the convergence of the physical and virtual worlds for building a real-world Metaverse. For physical-virtual synchronization, crowdsensing is applied to employ Internet of Things (IoT) devices to sense physical objects and collect status information for timely updating the associated DTs. However, an incentive mechanism is necessitated to stimulate the devices to perform sensing tasks while meeting task requirements such as Age of Information (AoI) requirements. Thus, we adopt a contract theoretic approach to design an incentive mechanism for the devices that are classified into several types and the type parameters are unknown to others. The contract consists of a set of contract items with each contract item representing a device type, which specifies the required data updating frequency and monetary reward. We formulate an optimal contract design problem subject to feasible constraints including the average AoI requirement for each device type. Furthermore, we solve the problem by transforming it into a convex optimization problem that can be directly tackled by the existing methods. Finally, we provide numerical results to demonstrate the effectiveness and efficiency of our scheme. © IFIP International Federation for Information Processing 2025.

关键词： Convex optimization

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EFNet: estimation of left ventricular ejection fraction from cardiac ultrasound videos using deep learning

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PeerJ computer science 2025年 11卷 e2506-e2506页

作者： Ali, Waqas Alsabban, Wesam Shahbaz, Muhammad Al-Laith, Ali Almogadwy, Bassam Computer Science Department University of Engineering and Technology Lahore Pakistan Department of Computer and Network Engineering College of Computing Umm Al-Qura University Makkah Saudi Arabia Computer Science Department University of Copenhagen Copenhagen Denmark Department of Artificial Intelligence and Data Science Taibah University Medina Saudi Arabia

The ejection fraction (EF) is a vital metric for assessing cardiovascular function through cardiac ultrasound. Manual evaluation is time-consuming and exhibits high variability among observers. Deep-learning techniques offer precise and autonomous EF predictions, yet these methods often lack explainability. Accurate heart failure prediction using cardiac ultrasound is challenging due to operator dependency and inconsistent video quality, resulting in significant interobserver variability. To address this, we developed a method integrating convolutional neural networks (CNN) and transformer models for direct EF estimation from ultrasound video scans. This article introduces a Residual Transformer Module (RTM) that extends a 3D ResNet-based network to analyze (2D + t) spatiotemporal cardiac ultrasound video scans. The proposed method, EFNet, utilizes cardiac ultrasound video images for end-to-end EF value prediction. Performance evaluation on the EchoNet-Dynamic dataset yielded a mean absolute error (MAE) of 3.7 and an R2 score of 0.82. Experimental results demonstrate that EFNet outperforms state-of-the-art techniques, providing accurate EF predictions. Copyright 2025 Ali et al.

关键词： Convolutional neural networks

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Artificial Intelligence Techniques for the Performance Analysis of Indian Mutual Fund Industry and Market Trends 6

Artificial Intelligence Techniques for the Performance Analy...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Priscila, S. Silvia Sakthivanitha, M. Yogeshwari, M. Devakirubai, T. Shirley Sirajudeen, M. Mohamed Department of Computer Science Bharath Institute of Higher Education and Research Tamil Nadu 600126 India Department of Information Technology VEL’S Institute of Science Technology and Advanced Studies Tamil Nadu Chennai India Department of Data Analyst Saveetha Liberal Arts and Science College Tamil Nadu Chennai India Department of Commerce Madras Christian College Tamil Nadu Chennai India Thaloor Tamil Nadu The Nilgiris643239 India

ISBN: (纸本)9798331522667

A basic examination of a mutual fund's strategy (growth or value), median market cap, rolling returns, standard deviation, and maybe a portfolio breakdown by industry, geography, and other factors make up a typical mutual fund analysis. Mutual fund analysis typically involves calculating the amount of management contribution to the portfolio and ranking equities according to industry. Analyzing a mutual fund portfolio involves a few phases, one of which comprehends the fund's design. This study examine the basic mutual fund parameters and explore the use of artificial intelligence (AI) methods, including data Mining, machine learning(ML), blockchain and predictive analytics in predicting market trends and assessing mutual fund performance. The purpose of this study is to give users a clear, concise, and professional overview of how to analyze the performance of mutual funds using AI Techniques. © 2025 IEEE.

关键词： Decentralized finance

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An Overview of IoT-based ECG Wavelength Analysis for Remote Patient Monitoring

An Overview of IoT-based ECG Wavelength Analysis for Remote ...

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Advancement in Computation & computer Technologies (InCACCT), International Conference on

作者： Kamrul Islam Najnin Sultana Nishu Md. Samiul Islam Md Tanvir Chowdhury Habibur Rahman Mahabubur Rahman Department of Computer Science and Engineering East West University Dhaka Bangladesh Department of Statistics and Data Science Jahangirnagar University Dhaka Bangladesh

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Adopting new IoT technologies holds great potential for changing the patient healthcare landscape. While IoT-enabled healthcare applications have become increasingly popular, it’s important to note that rural areas often face poor healthcare infrastructure and limited access to quality medical care. However, IoT is a valuable asset, especially in emergencies like Covid-19. To make IoT even more helpful in this area, we will explore the existing IoT systems for Remote Patient Monitoring (RPM), reveal their shortcomings, and provide systematic fixes. In this study, we look at the advantages of integrating IoT devices with sensors, cloud technologies, and ECG wavelength analysis, particularly in RPM. IoT-enabled devices are adept at collecting and sending real-time information, enabling healthcare professionals to remotely monitor vital signs, essential indicators, and health parameters. While there are challenges associated with empowering remote patient monitoring with IoT, it can also improve healthcare outcomes and reduce costs. This technology fusion provides a glimmer of hope during a pandemic, promoting not only better health but a more fulfilling life. To sum up, this inquiry prepares the ground for a future where healthcare is personalized and patient-centric, with a range of approaches, resilience in the face of failure, and triumphs in overcoming challenges.

关键词： Patient monitoring Cloud computing Medical services Electrocardiography Real-time systems Sensors Mobile applications Internet of Things Servers Faces

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Multi-Agent Deep Reinforcement Learning with Trajectory Prediction for Task Migration-Assisted Computation Offloading

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IEEE Transactions on Mobile Computing 2025年

作者： Zhang, Xinyi Wang, Chunyang Zhu, Yanmin Cao, Jian Liu, Tong Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China School of Data Science and Engineering East China Normal University Shanghai China School of Computer Engineering and Science Shanghai University Shanghai China

Multi-access edge computing has become an effective paradigm to provide offloading services for computation-intensive and delay-sensitive tasks on vehicles. However, high mobility of vehicles usually incurs spatio-temporal load-imbalances among edge servers. Therefore, task migration is employed to maintain dynamic workload balancing by transmitting excessive tasks from overloaded to underloaded servers. Recent studies adopt deep reinforcement learning approaches to generate offloading and migration decisions based on current observations of systems. However, we argue that the migration direction is highly dependent on vehicular movements, and task migration towards the wrong direction could lead to additional delays. Therefore, we emphasize the importance of guiding task migration via exploring prospective trajectories of vehicles. We propose a Mobility-Aware Cooperative Multi-Agent (MCMA) deep reinforcement learning approach to make vehicle-by-vehicle decisions in multi-edge computation offloading scenarios. A two-stage decision framework is designed to solve the joint optimization problem of computation offloading and resource allocation. Additionally, an Informer-based multi-step vehicular trajectory prediction module is incorporated to enhance the capability of forecasting vehicular movements. Extensive experiments and analysis are conducted on synthetic and realistic scenarios, showing that our approach consistently outperforms both heuristic and DRL-based methods. The simulation scenarios and source codes are publicly available here. © 2002-2012 IEEE All rights reserved.

关键词： Computation offloading

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Remote Sensing Scene Classification Based on Hybrid Convolutional Neural Network and Attention Module

Remote Sensing Scene Classification Based on Hybrid Convolut...

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Advancement in data science, E-learning and Information Systems (ICADEIS), International Conference on

作者： Muhammad Yusuf Kardawi Alexander Agung Santoso Gunawan Profesional Engineer Program Faculty of Engineering Bina Nusantara University Jakarta Indonesia Computer Science Department Faculty of Computer Science University of Indonesia Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331513320

ISBN: (纸本)9798331513337

Remote sensing images present classification challenges due to the complexity of their structural and spatial patterns. This research explores a hybrid approach that combines convolutional neural network (CNN) and attention through feature fusion to improve scene classification accuracy in remote sensing images. The proposed architecture utilizes EfficientNet and VGGNet to extract depth features separately. The extracted features are then integrated with Dynamic Selfattention (DSA), which dynamically focuses the model on the most relevant information in the image. DSA allows the model to adaptively assign weights to different parts of the image, thus improving the discriminative ability of the model. Furthermore, a feature fusion technique is applied to combine information from different layers of the CNN and DSA modules. Experiments conducted on the UC Merced dataset showed accuracies of 0.9181 and 0.9167. These results show that the combination of CNN, DSA, and feature fusion is an effective and robust approach for remote sensing image classification.

关键词： Adaptation models Scene classification Accuracy Electronic learning data science Feature extraction Convolutional neural networks data mining Remote sensing Information systems

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